# nfsdr: Nonparametric false simultaneous discovery rate control In ssa: Simultaneous Signal Analysis

## Description

Given D sequences of test statistics, returns the optimal square rejection that identifies the largest number of simultaneous signals while controlling the false discovery rate. Assumes a common threshold for each sequence.

## Usage

 `1` ```nfsdr(T, alpha, rho = 0, m = 5000, rescale = TRUE) ```

## Arguments

 `T` n x D matrix of test statistics that are stochastically larger under the null, where n is the number of features and D is the numberof sequences of test statistics `alpha` nominal false simultaneous discovery rate `rho` regularization parameter to guarantee asymptotic control of the false discovery rate; should be a small positive value, but rho = 0 works well in most simulations `m` search for the optimal threshold up to only the mth largest unique value of T; can speed up computation `rescale` apply rank transformation to the test statistics within each sequence such that they are of comparable scales

## Value

indices of the features delcared to be simultaneous signals

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## generate paired test statistics p <- 10^6; ## total number of pairs X <- c(rep(0,p-30),rep(1,10),rep(2,10),rep(3,10)); ## X=0: no signal in either sequence of tests ## X=1: signal in sequence 1 only ## X=2: signal in sequence 2 only ## X=3: simultaneous signal set.seed(1); Z1 <- rnorm(p,0,1); Z1[X==1|X==3] <- rnorm(20,3,1); Z2 <- rnorm(p,0,1); Z2[X==2|X==3] <- rnorm(20,4,1); T <- cbind(Z1^2, Z2^2); ## rejected simultaneous signals nfsdr(T, 0.05) ```

ssa documentation built on May 1, 2019, 10:27 p.m.